Similarity-Based Pattern Analysis and Recognition

Similarity-Based Pattern Analysis and Recognition
Author: Marcello Pelillo
Publisher: Springer Science & Business Media
Total Pages: 293
Release: 2013-11-26
Genre: Computers
ISBN: 1447156285

Download Similarity-Based Pattern Analysis and Recognition Book in PDF, Epub and Kindle

This accessible text/reference presents a coherent overview of the emerging field of non-Euclidean similarity learning. The book presents a broad range of perspectives on similarity-based pattern analysis and recognition methods, from purely theoretical challenges to practical, real-world applications. The coverage includes both supervised and unsupervised learning paradigms, as well as generative and discriminative models. Topics and features: explores the origination and causes of non-Euclidean (dis)similarity measures, and how they influence the performance of traditional classification algorithms; reviews similarity measures for non-vectorial data, considering both a “kernel tailoring” approach and a strategy for learning similarities directly from training data; describes various methods for “structure-preserving” embeddings of structured data; formulates classical pattern recognition problems from a purely game-theoretic perspective; examines two large-scale biomedical imaging applications.

Similarity-Based Pattern Recognition

Similarity-Based Pattern Recognition
Author: Edwin Hancock
Publisher: Springer
Total Pages: 307
Release: 2013-06-28
Genre: Computers
ISBN: 3642391400

Download Similarity-Based Pattern Recognition Book in PDF, Epub and Kindle

This book constitutes the proceedings of the Second International Workshop on Similarity Based Pattern Analysis and Recognition, SIMBAD 2013, which was held in York, UK, in July 2013. The 18 papers presented were carefully reviewed and selected from 33 submissions. They cover a wide range of problems and perspectives, from supervised to unsupervised learning, from generative to discriminative models, from theoretical issues to real-world practical applications, and offer a timely picture of the state of the art in the field.

Similarity-Based Pattern Recognition

Similarity-Based Pattern Recognition
Author: Aasa Feragen
Publisher: Springer
Total Pages: 238
Release: 2015-10-04
Genre: Computers
ISBN: 331924261X

Download Similarity-Based Pattern Recognition Book in PDF, Epub and Kindle

This book constitutes the proceedings of the Third International Workshop on Similarity Based Pattern Analysis and Recognition, SIMBAD 2015, which was held in Copenahgen, Denmark, in October 2015. The 15 full and 8 short papers presented were carefully reviewed and selected from 30 submissions.The workshop focus on problems, techniques, applications, and perspectives: from supervisedto unsupervised learning, from generative to discriminative models, and fromtheoretical issues to empirical validations.

Similarity-Based Pattern Recognition

Similarity-Based Pattern Recognition
Author: Marcello Pelillo
Publisher: Springer
Total Pages: 345
Release: 2011-09-25
Genre: Computers
ISBN: 3642244718

Download Similarity-Based Pattern Recognition Book in PDF, Epub and Kindle

This book constitutes the proceedings of the First International Workshop on Similarity Based Pattern Recognition, SIMBAD 2011, held in Venice, Italy, in September 2011. The 16 full papers and 7 poster papers presented were carefully reviewed and selected from 35 submissions. The contributions are organized in topical sections on dissimilarity characterization and analysis; generative models of similarity data; graph-based and relational models; clustering and dissimilarity data; applications; spectral methods and embedding.

Similarity-Based Pattern Recognition

Similarity-Based Pattern Recognition
Author: Aasa Feragen
Publisher:
Total Pages:
Release: 2015
Genre:
ISBN: 9783319242620

Download Similarity-Based Pattern Recognition Book in PDF, Epub and Kindle

This book constitutes the proceedings of the Third International Workshop on Similarity Based Pattern Analysis and Recognition, SIMBAD 2015, which was held in Copenahgen, Denmark, in October 2015. The 15 full and 8 short papers presented were carefully reviewed and selected from 30 submissions.The workshop focus on problems, techniques, applications, and perspectives: from supervised to unsupervised learning, from generative to discriminative models, and from theoretical issues to empirical validations.

Similarity-Based Pattern Recognition

Similarity-Based Pattern Recognition
Author: Marcello Pelillo
Publisher: Springer Science & Business Media
Total Pages: 345
Release: 2011-09-21
Genre: Computers
ISBN: 364224470X

Download Similarity-Based Pattern Recognition Book in PDF, Epub and Kindle

This book constitutes the proceedings of the First International Workshop on Similarity Based Pattern Recognition, SIMBAD 2011, held in Venice, Italy, in September 2011. The 16 full papers and 7 poster papers presented were carefully reviewed and selected from 35 submissions. The contributions are organized in topical sections on dissimilarity characterization and analysis; generative models of similarity data; graph-based and relational models; clustering and dissimilarity data; applications; spectral methods and embedding.

Machine Learning and Data Mining in Pattern Recognition

Machine Learning and Data Mining in Pattern Recognition
Author: Petra Perner
Publisher: Springer
Total Pages: 452
Release: 2003-08-02
Genre: Computers
ISBN: 3540450653

Download Machine Learning and Data Mining in Pattern Recognition Book in PDF, Epub and Kindle

TheInternationalConferenceonMachineLearningandDataMining(MLDM)is the third meeting in a series of biennial events, which started in 1999, organized by the Institute of Computer Vision and Applied Computer Sciences (IBaI) in Leipzig. MLDM began as a workshop and is now a conference, and has brought the topic of machine learning and data mining to the attention of the research community. Seventy-?ve papers were submitted to the conference this year. The program committeeworkedhardtoselectthemostprogressiveresearchinafairandc- petent review process which led to the acceptance of 33 papers for presentation at the conference. The 33 papers in these proceedings cover a wide variety of topics related to machine learning and data mining. The two invited talks deal with learning in case-based reasoning and with mining for structural data. The contributed papers can be grouped into nine areas: support vector machines; pattern dis- very; decision trees; clustering; classi?cation and retrieval; case-based reasoning; Bayesian models and methods; association rules; and applications. We would like to express our appreciation to the reviewers for their precise andhighlyprofessionalwork.WearegratefultotheGermanScienceFoundation for its support of the Eastern European researchers. We appreciate the help and understanding of the editorial sta? at Springer Verlag, and in particular Alfred Hofmann,whosupportedthepublicationoftheseproceedingsintheLNAIseries. Last, but not least, we wish to thank all the speakers and participants who contributed to the success of the conference.

Structural, Syntactic, and Statistical Pattern Recognition

Structural, Syntactic, and Statistical Pattern Recognition
Author: Niels da Vitoria Lobo
Publisher: Springer Science & Business Media
Total Pages: 1029
Release: 2008-11-24
Genre: Computers
ISBN: 3540896880

Download Structural, Syntactic, and Statistical Pattern Recognition Book in PDF, Epub and Kindle

This book constitutes the refereed proceedings of the 12th International Workshop on Structural and Syntactic Pattern Recognition, SSPR 2008 and the 7th International Workshop on Statistical Techniques in Pattern Recognition, SPR 2008, held jointly in Orlando, FL, USA, in December 2008 as a satellite event of the 19th International Conference of Pattern Recognition, ICPR 2008. The 56 revised full papers and 42 revised poster papers presented together with the abstracts of 4 invited papers were carefully reviewed and selected from 175 submissions. The papers are organized in topical sections on graph-based methods, probabilistic and stochastic structural models for PR, image and video analysis, shape analysis, kernel methods, recognition and classification, applications, ensemble methods, feature selection, density estimation and clustering, computer vision and biometrics, pattern recognition and applications, pattern recognition, as well as feature selection and clustering.